Python 操作列时如何使用熊猫数据框处理“除以零”？
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How to deal with "divide by zero" with pandas dataframes when manipulating columns?
提问by ShanZhengYang
I'm working with hundreds of pandas dataframes. A typical dataframe is as follows:
我正在使用数百个熊猫数据框。一个典型的数据框如下：
import pandas as pd
import numpy as np
data = 'filename.csv'
df = pd.DataFrame(data)
df
one two three four five
a 0.469112 -0.282863 -1.509059 bar True
b 0.932424 1.224234 7.823421 bar False
c -1.135632 1.212112 -0.173215 bar False
d 0.232424 2.342112 0.982342 unbar True
e 0.119209 -1.044236 -0.861849 bar True
f -2.104569 -0.494929 1.071804 bar False
....
There are certain operations whereby I'm dividing between columns values, e.g.
有某些操作我可以在列值之间进行划分，例如
df['one']/df['two']
However, there are times where I am dividing by zero, or perhaps both
但是，有时我会除以零，或者两者都除
df['one'] = 0
df['two'] = 0
Naturally, this outputs the error:
自然，这会输出错误：
ZeroDivisionError: division by zero
I would prefer for 0/0 to actually mean "there's nothing here", as this is often what such a zero means in a dataframe.
我更希望 0/0 实际上意味着“这里什么都没有”，因为这通常是数据帧中这样的零的含义。
(a) How would I code this to mean "divide by zero" is 0 ?
(a) 我将如何编码这意味着“除以零”是 0 ？
(b) How would I code this to "pass" if divide by zero is encountered?
(b) 如果遇到除以零，我将如何将其编码为“通过”？
采纳答案by vielmetti
Two approaches to consider:
需要考虑的两种方法：
Prepare your data so that never has a divide by zero situation, by explicitly coding a "no data" value and testing for that.
通过明确编码“无数据”值并为此进行测试，准备好您的数据，以便永远不会出现除以零的情况。
Wrap each division that might result in an error with a try/exceptpair, as described at https://wiki.python.org/moin/HandlingExceptions(which has a divide by zero example to use)
如https://wiki.python.org/moin/HandlingExceptions 中所述，将可能导致错误的每个分区用try/except对包裹起来（使用除以零示例）
(x,y) = (5,0)
try:
z = x/y
except ZeroDivisionError:
print "divide by zero"
I worry about the situation where your data includes a zero that's really a zero (and not a missing value).
我担心您的数据包含一个实际上是零（而不是缺失值）的零的情况。
回答by Alexander
It would probably be more useful to use a dataframe that actually has zero in the denominator (see the last row of column two).
使用分母实际上为零的数据帧可能更有用（请参阅 column 的最后一行two）。
one two three four five
a 0.469112 -0.282863 -1.509059 bar True
b 0.932424 1.224234 7.823421 bar False
c -1.135632 1.212112 -0.173215 bar False
d 0.232424 2.342112 0.982342 unbar True
e 0.119209 -1.044236 -0.861849 bar True
f -2.104569 0.000000 1.071804 bar False
>>> df.one / df.two
a -1.658442
b 0.761639
c -0.936904
d 0.099237
e -0.114159
f -inf # <<< Note division by zero
dtype: float64
When one of the values is zero, you should get infor -infin the result. One way to convert these values is as follows:
当其中一个值为零时，您应该得到inf或-inf在结果中。转换这些值的一种方法如下：
df['result'] = df.one.div(df.two)
df.loc[~np.isfinite(df['result']), 'result'] = np.nan # Or = 0 per part a) of question.
# or df.loc[np.isinf(df['result']), ...
>>> df
one two three four five result
a 0.469112 -0.282863 -1.509059 bar True -1.658442
b 0.932424 1.224234 7.823421 bar False 0.761639
c -1.135632 1.212112 -0.173215 bar False -0.936904
d 0.232424 2.342112 0.982342 unbar True 0.099237
e 0.119209 -1.044236 -0.861849 bar True -0.114159
f -2.104569 0.000000 1.071804 bar False NaN
回答by Kartik
df['one'].divide(df['two'])
Code:
代码：
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(5,2), columns=list('ab'))
df.loc[[1,3], 'b'] = 0
print(df)
print(df['a'].divide(df['b']))
Result:
结果：
a b
0 0.517925 0.305973
1 0.900899 0.000000
2 0.414219 0.781512
3 0.516072 0.000000
4 0.841636 0.166157
0 1.692717
1 inf
2 0.530023
3 inf
4 5.065297
dtype: float64
回答by Christian
You can always use a try statement:
您始终可以使用 try 语句：
try:
z = var1/var2
except ZeroDivisionError:
print ("0") #As python-3's rule is: Parentheses
OR...
或者...
You can also do:
你也可以这样做：
if var1==0:
if var2==0:
print("0")
else:
var3 = var1/var2
Hope this helped! Choose whichever choice you desire (they're both the same anyways).
希望这有帮助！选择您想要的任何选择（无论如何它们都是相同的）。
回答by Merlin
Try this:
尝试这个：
df['one']/(df['two'] +.000000001)
